Nathalie Risso

Assistant Professor, School of Mining Engineering & Mineral Resources
Member of the Graduate Faculty

Nathalie Risso is a tenure-track assistant professor with the School of Mining Engineering and Mineral Resources at the University of Arizona. She directs the Mine Automation and Autonomous Systems Laboratory, which focuses on integrating automation under a cyber-physical systems approach to enable autonomous behavior in safety-critical environments such as mining applications. Her research emphasizes developing solutions for mining applications in harsh, low-connectivity environments where safety, robustness and autonomous systems collaboration are design requirements. Risso is a 2023 recipient of the SME Freeport-McMoRan Inc. Career Development Grant to develop research related to AI-driven cyber-physical systems for mining applications. She has extensive consulting experience developing automation and autonomous systems solutions for the mining and energy industries. Risso has led several research and development industry and government initiatives in AI, machine learning and advanced control systems.

Degrees

  • PhD Electrical and Computer Engineering
    • University of Arizona, United States
  • MS Electrical and Computer Engineering
    • University of Arizona, United States
  • BS Electrical Engineering
    • Universidad de Concepcion, Concepcion, Chile

Work Experience

  • Engineering Consultant, Freelance, (2017-Ongoing)
  • Graduate Research Assistant, The University of Arizona, (2013-2019)
  • Process Control Engineer Intern, Freeport-McMoRan, (2017-2017)
  • Instructor Universidad del Bio-Bio, (2007-2013)
  • Electric Engineer, Engineering Consulting JR, (2010-2011)

Interests

Teaching

Electric circuits, artificial intelligence and machine learning, computer vision, control systems, mine automation, robotics and autonomous systems, STEM education

Research

Cyber-physical systems, automation and autonomy, artificial intelligence and machine learning, energy efficiency, energy innovation, robotics, advanced process control systems, optimal control, computer vision, mine automation, fleet management

Courses

View the complete course list in UA Profiles.

Licensure & Certification

  • Life-Cycle Management of Tailings Facilities, The Tailings Center at Colorado School of Mines, 2025
  • Executive Space Course, International Space University, Oslo, Norway, 2024
  • Teaching Computation with Matlab and GenAI, Mathworks & Carleton College 2024
  • International Engineering Educator, International Society of Engineering Pedagogy (IGIP), 2021
  • Energy Innovation and Emerging Technologies, Stanford University, 2020

Select

Journals/Publications

  • M. Saavedra, N. Risso, M. Momayez, R. Nunes, V. Tenorio, J. Zhang. Blending Characterization for Effective Management in Mining Operations. Minerals 15 (9), 891Proceedings, 2025.
  • E. Wellman, D. Riley, A. Hughes, N. Risso, M. Momayez, J. Kemeny. A Proposed Concept for Classifying Uniaxial Compressive Strength (Ucs) from Swir Hyperspectral Data. Engineering Geology 356, September 2025.
  • P. Lopez, N. Risso, A. Anani, M. Momayez. Geohazard Identification in Underground Mines: A Mobile App. Sensors 24 (24), 8052, 2024.
  • A. Anani, S.O. Adewuyi, N. Risso, W. Nyaaba. Advancements in machine learning techniques for coal and gas outburst prediction in underground mines. International Journal of Coal Geology 285, 104471, 2024.
  • P. Lopez, I. Reyes, N. Risso, M. Momayez, J Zhang. Machine Learning Algorithms for Semi-Autogenous Grinding Mill Operational Regions’ Identification. Minerals 13 (11), 1360, 2023.
  • N. Risso, B. Altin, R.G. Sanfelice, J. Sprinkle. Set-valued model predictive control. Computation-Aware Algorithmic Design for Cyber-Physical Systems, 187-207, 2023.
  • C. Olmos-de-Aguilera, P.G. Campos, N. Risso. Error reduction in long-term mine planning estimates using deep learning models. Expert Systems with Applications, 119487, 2023.

Proceedings Publications

  • J. He, N. Risso, T. Bettencourt, A. Anani. Mining the Text: Automating Safety Insights from Mining Accident Reports. Proceedings of the 10th International Conference on Machine Learning Technologies (ICMLT), 341-349, 2025.
  • N. Risso, M. Saavedra, J. Zhang. Mine2Twin: A Synergistic Industry-Academia Collaboration to Improve Engineering Skills for Industry 5.0. 2025 IEEE Global Engineering Education Conference (EDUCON), 2025.
  • T. Chimbwanda, A. Anani, N. Risso. Discrete Events Simulation Approach to Investigating the Impact of Electrifying Haul Trucks: A Case Study. Proceedings of the International Conference on Application of Computers & Operations Research in the Minerals Industry (APCOM), 2025.
  • N. Risso, L. Cheng, J. He. A Computer Vision-based Platform for Automatic PPE Detection in Underground Environments. Proceedings of the 9th International Conference on Machine Learning Technologies (ICMLT), 2024.
  • C. Olmos, N. Risso, A. Anani. Camera-aided Technology for Underground Mine Safety (CAT-UMS), Proceedings of the International Conference on Application of Computers & Operations Research in the Minerals Industry (APCOM), 2023.
  • A. Palma, A. Reyes, J. Rohten, N. Risso, D. Quezada, V. Esparza. MPC-based Traction Control for Electric Vehicles. 2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA).